Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

Summary

Medication errors are common, life-threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. In this study, investigators would perform a cluster randomized controlled trial of a clinical reminding system that uses DNN and Probabilistic models to detect and notify physicians of inappropriate prescriptions, giving them the opportunity to correct these gaps and increase prescriptions completeness. This study aim is to assess whether or not this system would improve prescription notation for a broad array of patient conditions.

Conditions

• Hypertension

Interventions

OTHER

AESOP service system

Investigators develop an electronic reminder in CPOE system which notifies physicians when there appears to be an inappropriate prescription. At the time, a physician saves a typed prescription, our system analyzes the patient's medications, diseases and uses the knowledge base to determine whether a medication is uncommonly prescribed to all diseases in a given prescription. If the system detects the common associations of medications and diseases in a given prescription, it considers an appropriate prescription, and, if not, an actionable reminder is shown onscreen. To the right of each suggested uncommon medication is a reason why the reminder is appearing. Physicians can accept the reminder or ignore the reminder.

Sponsors & Collaborators

• Ministry of Science and Technology, Taiwan

  collaborator OTHER_GOV

• Taipei Medical University Shuang Ho Hospital

  collaborator OTHER

• Taiwan College of Healthcare Executives

  collaborator UNKNOWN

• Taipei Medical University Taipei Municipal Wan Fang Hospital

  collaborator UNKNOWN

• Case Western Reserve University

  collaborator OTHER

• Cardinal Tien Hospital

  collaborator OTHER

• Yong He Cardinal Tien Hospital

  collaborator UNKNOWN

• Chang Hua Christian Hospital

  collaborator UNKNOWN

• Taipei Medical University Hospital

  collaborator OTHER

• Taipei Medical University

  lead OTHER

Principal Investigators

• Yu-Chuan MD Li, PhD · Taipei Medical University

• Chuya Huang, MA · Taipei Medical University

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2017-05-01

Primary Completion

2017-12-31

Completion

2018-02-28

Countries

• Taiwan

Study Locations